In general, deep learning is defined as a set of machine learning algorithms that try to achieve a high level of abstraction through a combination of various nonlinear transformation techniques, and is a field of machine learning that teaches computers how to think like people do.
A number of researches have been carried out to express data in the form that the computers can understand, for example, pixel information of an image as a column vector, and to apply it to the machine learning. As a result of this effort, a variety of deep learning techniques such as deep neural networks, convolutional neural networks, and recurrent neural networks have been applied to various fields like computer vision, voice recognition, natural language processing, and voice/signal processing, etc., and high performing deep learning networks have been developed.
Object detectors for analyzing images using the deep learning and detecting objects in the images have been applied to many industrial fields.
In particular, the object detectors are used for autonomous vehicles, mobile devices, surveillance systems, and the like, thereby improving user convenience and stability.
However, since conventional object detectors detect objects according to their learned parameters, it cannot be confirmed whether the objects are accurately detected in an actual situation, and thus the conventional object detectors require separate monitoring for the confirmation.
Also, the conventional object detectors have difficulty in securing various training data for improving performance.
In addition, since a system using the conventional object detectors can confirm only directions that a camera can see and scenes not occluded by obstacles, in case of blind spots and occluded scenes, the system cannot identify the actual situation.
In addition, the conventional object detectors may output different results depending on the learned parameters, so there may be discrepancies between information detected by other systems nearby.